{ "_id": "6acd139da14063b82c3ab1b2", "Package": "hypergraphs", "Title": "Higher-Order Network Analysis", "Version": "0.7.5", "Authors@R": "c(\nperson(\"Mohammed\", \"Saqr\", email = \"saqr@saqr.me\", role = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0001-5881-3109\"))\n)", "Description": "Build and analyze higher-order networks, in which a\nrelation can involve more than two nodes or can depend on more\nthan the current node. Covers the three structure families of\nthe higher-order network literature Battiston et al. (2020)\n, plus a text family that\nreads a corpus as a hypergraph. Every object is read through\none accessor, and every sequence-taking verb reads long, wide,\nlist or model input. Memory networks from categorical\nsequences: higher-order networks with rule extraction Xu,\nWickramarathne, and Chawla (2016) ,\nhigher-order network embedding Saebi et al. (2020)\n, hypergeometric path anomaly\ndetection LaRock et al. (2020)\n, the multi-order generative\nmodel Scholtes (2017) ,\nhigher-order centralities Scholtes, Wider, and Garas (2016)\n, a permutation-based Markov\norder test, a path-dependence diagnostic, and bootstrap and\npermutation inference for extracted rules. Simplicial\ncomplexes: clique, Vietoris-Rips and pathway complexes, Betti\nnumbers, persistent homology, persistence landscapes,\nbottleneck and Wasserstein distances, and q-analysis.\nHypergraphs: construction from network cliques, sliding windows\nover sequences, bipartite group membership, or embedding\nnearest neighbours; structural measures; tensor eigenvector\ncentralities Benson (2019) ; PageRank\nwith edge-dependent vertex weights Chitra and Raphael (2019)\n; the weighted normalized\nhypergraph Laplacian of Zhou, Huang, and Scholkopf (2006)\n with spectral, symmetric\nnon-negative matrix factorization (NMF), and joint-NMF\nclustering Hayashi et al. (2020) \nand transductive label spreading; projections to weighted\ngraphs and s-line graphs; Bernoulli-incidence,\nstochastic-block, uniform and regular random generators;\ntemporal snapshots; hyperedge betweenness and closeness;\nInfomap ensembles with adjusted- mutual-information medoid\nselection; Y/T/O motif profiles and null models by\ndegree-preserving swaps and the configuration model; and\nhypergraph neural networks in native 'torch': the hypergraph\nneural network (HGNN), hypergraph attention networks (HyperGAT\nand its heterogeneous variant), the hypergraph convolutional\nnetwork (HyperGCN), hypergraph networks with hyperedge neurons\n(HNHN), AllDeepSets and AllSetTransformer. Text hypergraphs: a\ncorpus becomes a weighted document-word hypergraph in either\norientation (bag of words with smoothed term frequency-inverse\ndocument frequency (tf-idf), token windows, or embedding\nnearest neighbours), analysed with tidy verbs for clustering,\nkeyword extraction, few-label classification, and null testing.", "License": "MIT + file LICENSE", "URL": "https://github.com/mohsaqr/hypergraphs,\nhttps://pak.dynasite.org/hypergraphs/", "BugReports": "https://github.com/mohsaqr/hypergraphs/issues", "Language": "en-US", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "Additional_repositories": "https://mohsaqr.r-universe.dev", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "LazyData": "true", "Repository": "https://mohsaqr.r-universe.dev", "Date/Publication": "2026-10-12 13:21:40 UTC", "RemoteUrl": "https://github.com/mohsaqr/hypergraphs", "RemoteRef": "HEAD", "RemoteSha": "9ec1d27f7ab7ced4ef4e15664ecb46a23450eac3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-12 16:51:45 UTC", "User": "root" }, "Author": "Mohammed Saqr [aut, cre, cph] (ORCID:\n)", "Maintainer": "Mohammed Saqr ", "_user": "mohsaqr", "_type": "src", "_file": "hypergraphs_0.7.5.tar.gz", "_fileid": "https://r2.ropensci.org/d5e4e3107f78c3b1e5d62b60fc12bc760cd7cba92360d66a6890f7783ade498e", "_filesize": 5537645, "_sha256": "d5e4e3107f78c3b1e5d62b60fc12bc760cd7cba92360d66a6890f7783ade498e", "_expires": "2027-01-20T17:06:33.000Z", "_created": "2026-10-12T16:51:45.000Z", "_published": "2026-10-12T17:06:37.581Z", "_buildurl": "https://github.com/r-universe/mohsaqr/actions/runs/38279465617", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 114916091952, "time": 310, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "ERROR", "artifact": "11761012441" }, { "job": 114916091832, "time": 270, "config": "linux-release-x86_64", "r": "4.6.1", "check": "ERROR", "artifact": "11761012327" }, { "job": 114916092009, "time": 244, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "ERROR", "artifact": "11761143847" }, { "job": 114916091816, "time": 205, "config": "macos-release-arm64", "r": "4.6.1", "check": "ERROR", "artifact": "11761008688" }, { "job": 114913201450, "time": 458, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "11760558225" }, { "job": 114916091883, "time": 123, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "11761630129" }, { "job": 114916091943, "time": 420, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "ERROR", "artifact": "11761755936" }, { "job": 114916091944, "time": 340, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "ERROR", "artifact": "11760429108" }, { "job": 114916091852, "time": 355, "config": "windows-release-x86_64", "r": "4.6.1", "check": "ERROR", "artifact": "11761017911" } ], "_upstream": "https://github.com/mohsaqr/hypergraphs", "_commit": { "id": "9ec1d27f7ab7ced4ef4e15664ecb46a23450eac3", "author": "mohsaqr ", "committer": "mohsaqr ", "message": "hypergraphs 0.7.5: corrected HYPA for sequences and hypergraphs\n\n- hypa() on sequences uses the corrected Nestimate estimator (>= 0.9.26)\n and defaults to p_adjust = \"none\", as in LaRock et al. (2020).\n- hypa.net_hg(): the pair null now preserves every node's co-occurrence\n strength (fitted rank-one model over all pairs, O(n) per sweep), uses the\n strict over tail P(X > f), gates over on observed and under on expected\n counts so pairs that never co-occur can be under-represented, adjusts over\n every possible pair when a correction is requested, and validates input.\n- hg_get(what = \"scores\", order_by = \"sig\") ranked by the lower tail only,\n putting the most over-represented paths last; it ranks by min(p_under,\n p_over).\n- Docs take their HYPA figures from inline R.\n", "time": 1791811300 }, "_maintainer": { "name": "Mohammed Saqr", "email": "saqr@saqr.me", "login": "loxavian", "description": "", "uuid": 52792889, "orcid": "0000-0001-5881-3109" }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.4", "role": "Depends" }, { "package": "cograph", "version": ">= 2.7.2", "role": "Imports" }, { "package": "ggplot2", 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posts whose topics follow second-order memory", "topics": [ "forum_posts" ] }, { "page": "gfcc_citations", "title": "Citation blocks of the German Federal Constitutional Court", "topics": [ "gfcc_citations" ] }, { "page": "gfcc_decisions", "title": "Decisions of the German Federal Constitutional Court", "topics": [ "gfcc_decisions" ] }, { "page": "group_hypergraph", "title": "Hypergraph from membership data or an edge list", "topics": [ "group_hypergraph" ] }, { "page": "heterogeneous_hgat", "title": "Heterogeneous graph attention classifier (HGAT)", "topics": [ "heterogeneous_hgat" ] }, { "page": "hg_agreement", "title": "Agreement between two labelings of the same nodes", "topics": [ "hg_agreement" ] }, { "page": "hg_allset", "title": "AllSet hypergraph node classifier", "topics": [ "hg_allset" ] }, { "page": "hg_assortativity", "title": "Degree assortativity of a hypergraph", "topics": [ "hg_assortativity" ] }, { "page": "hg_betti", "title": "Betti numbers of a simplicial complex", "topics": [ "hg_betti" ] }, { "page": "hg_bootstrap", "title": "Bootstrap inference for higher-order network rules", "topics": [ "hg_bootstrap" ] }, { "page": "hg_bottleneck", "title": "Bottleneck distance between persistence diagrams", "topics": [ "hg_bottleneck" ] }, { "page": "hg_centrality", "title": "Centralities of a hypergraph or a memory network", "topics": [ "hg_centrality", "hg_centrality.default" ] }, { "page": "hg_centrality.net_hg", "title": "Hypergraph eigenvector centralities", "topics": [ "hg_centrality.net_hg" ] }, { "page": "hg_centrality.net_hon", "title": "Higher-order centralities with first-order projection", "topics": [ "hg_centrality.net_hon" ] }, { "page": "hg_classify", "title": "Transductive label spreading on a hypergraph", "topics": [ "hg_classify" ] }, { "page": "hg_cluster", "title": "Spectral or symmetric-NMF clustering of hypergraph vertices", "topics": [ "hg_cluster" ] }, { "page": "hg_cocluster", "title": "Co-clustering of nodes and hyperedges (documents and words)", "topics": [ "hg_cocluster" ] }, { "page": "hg_communities", "title": "Communities of a hypergraph or a memory network", "topics": [ "hg_communities", "hg_communities.default" ] }, { "page": "hg_communities.net_hg", "title": "Stable Infomap communities of a hypergraph projection", "topics": [ "hg_communities.net_hg" ] }, { "page": "hg_communities.net_hon", "title": "Memory-network communities with the map equation", "topics": [ "hg_communities.net_hon" ] }, { "page": "hg_community_quality", "title": "Quality of a projected-hypergraph partition", "topics": [ "hg_community_quality" ] }, { "page": "hg_compare", "title": "Permutation comparison of the rules of two groups", "topics": [ "hg_compare" ] }, { "page": "hg_compare_communities", "title": "Compare community structure across representations", "topics": [ "hg_compare_communities", "hg_get.hypergraphs_community_comparison", "plot.hypergraphs_community_comparison", "print.hypergraphs_community_comparison" ] }, { "page": "hg_degree", "title": "Simplicial degree of every node", "topics": [ "hg_degree" ] }, { "page": "hg_degree_correlation", "title": "Correlation of hyperdegrees across interaction orders", "topics": [ "hg_degree_correlation" ] }, { "page": "hg_dictionary", "title": "Label nodes with a dictionary of terms", "topics": [ "hg_dictionary" ] }, { "page": "hg_edge_centrality", "title": "s-betweenness and s-closeness centrality of hyperedges", "topics": [ "hg_edge_centrality" ] }, { "page": "hg_edges", "title": "Hyperedge-level measures, as a tidy table", "topics": [ "hg_edges" ] }, { "page": "hg_embed", "title": "Low-Dimensional Hypergraph Embedding", "topics": [ "hg_embed" ] }, { "page": "hg_euler", "title": "Euler characteristic of a simplicial complex", "topics": [ "hg_euler" ] }, { "page": "hg_get", "title": "Read a table from any hypergraphs object", "topics": [ "hg_get", "hg_get.default", "hg_get.net_hg_pairwise" ] }, { "page": "hg_get.hg_classification", "title": "Read a classification result", "topics": [ "hg_get.hg_classification", "plot.hg_classification", "print.hg_classification", "summary.hg_classification" ] }, { "page": "hg_get.hg_hypergat", "title": "Read a fitted HyperGAT classifier", "topics": [ "hg_get.hg_hypergat", "plot.hg_hypergat", "print.hg_hypergat", "summary.hg_hypergat" ] }, { "page": "hg_get.hypergraphs_bootstrap", "title": "Inference table of a memory-network bootstrap", "topics": [ "hg_get.hypergraphs_bootstrap" ] }, { "page": "hg_get.hypergraphs_comparison", "title": "Edge table of a memory-network comparison", "topics": [ "hg_get.hypergraphs_comparison" ] }, { "page": "hg_get.net_hypa", "title": "Tables of a path-anomaly (HYPA) fit", "topics": [ "hg_get.hypergraphs_hypa", "hg_get.net_hypa" ] }, { "page": "hg_get.hypergraphs_memory_communities", "title": "Tidy tables of a memory-network community result", "topics": [ "hg_get.hypergraphs_memory_communities" ] }, { "page": "print.hypergraphs_result", "title": "Print a result of an imported estimator", "topics": [ "hg_get.hypergraphs_result", "plot.hypergraphs_result", "print.hypergraphs_result" ] }, { "page": "result-summary", "title": "Summaries of results", "topics": [ "hg_get.hypergraphs_summary", "print.hypergraphs_summary", "result-summary", "summary.hg_communities", "summary.hypergraphs_bootstrap", "summary.hypergraphs_bootstrap_group", "summary.hypergraphs_community_comparison", "summary.hypergraphs_comparison", "summary.hypergraphs_hypa", "summary.hypergraphs_memory_communities", "summary.hypergraphs_memory_group", "summary.hypergraphs_motifs", "summary.hypergraphs_result", "summary.hypergraphs_simplicial", "summary.net_hg_mmsbm", "summary.net_hg_topics" ] }, { "page": "hg_get.net_hg", "title": "Tables of a hypergraph", "topics": [ "hg_get.net_hg" ] }, { "page": "hg_get.net_hg_cluster", "title": "Assignment table of a hypergraph clustering fit", "topics": [ "hg_get.net_hg_cluster" ] }, { "page": "hg_get.net_hg_measures", "title": "Tables of a hypergraph measures fit", "topics": [ "hg_get.net_hg_measures" ] }, { "page": "hg_get.net_hg_transduction", "title": "Tables of a hypergraph transduction fit", "topics": [ "hg_get.net_hg_transduction" ] }, { "page": "hg_get.net_hon", "title": "Tables of a higher-order network", "topics": [ "hg_get.net_hon" ] }, { "page": "hg_get.net_honem", "title": "Tables of a higher-order network embedding", "topics": [ "hg_get.net_honem" ] }, { "page": "hg_get.net_markov_order", "title": "Tables of a Markov order test", "topics": [ "hg_get.net_markov_order" ] }, { "page": "hg_get.net_markov_order_group", "title": "Tables of a grouped Markov order test", "topics": [ "hg_get.net_markov_order_group" ] }, { "page": "hg_get.net_markov_stability", "title": "Tables of a Markov stability analysis", "topics": [ "hg_get.net_markov_stability" ] }, { "page": "hg_get.net_mogen", "title": "Tables of a multi-order generative model", "topics": [ "hg_get.net_mogen" ] }, { "page": "hg_get.net_path_dependence", "title": "Per-context table of a path-dependence analysis", "topics": [ "hg_get.net_path_dependence" ] }, { "page": "hg_get.net_temporal_hypergraph", "title": "Tidy tables of a temporal hypergraph", "topics": [ "hg_get.net_temporal_hypergraph" ] }, { "page": "hg_get.persistence_landscape", "title": "Table of a persistence landscape", "topics": [ "hg_get.persistence_landscape" ] }, { "page": "hg_get.persistent_homology", "title": "Tables of a persistent homology result", "topics": [ "hg_get.persistent_homology" ] }, { "page": "hg_get.q_analysis", "title": "Tables of a q-analysis", "topics": [ "hg_get.q_analysis" ] }, { "page": "hg_get.simplicial_complex", "title": "Tables of a simplicial complex", "topics": [ "hg_get.simplicial_complex" ] }, { "page": "hg_get.text_hypergraph", "title": "Tidy tables of a text hypergraph", "topics": [ "hg_get.text_hypergraph" ] }, { "page": "hg_growth", "title": "Growth of a temporal hypergraph over time", "topics": [ "hg_growth", "plot.hypergraphs_series" ] }, { "page": "hg_hnhn", "title": "HNHN node classifier", "topics": [ "hg_hnhn" ] }, { "page": "hg_homology", "title": "Persistent homology of a weighted network or a distance matrix", "topics": [ "hg_homology" ] }, { "page": "hg_hypergat", "title": "HyperGAT document classifier", "topics": [ "hg_hypergat", "text_hypergat" ] }, { "page": "hg_hypergcn", "title": "HyperGCN node classifier", "topics": [ "hg_hypergcn" ] }, { "page": "hg_joint_cluster", "title": "Joint NMF clustering with an auxiliary vertex relation", "topics": [ "hg_joint_cluster" ] }, { "page": "hg_keywords", "title": "Characteristic words (keywords) per cluster", "topics": [ "hg_keywords", "plot.hypergraphs_keywords", "print.hypergraphs_keywords" ] }, { "page": "hg_landscape", "title": "Persistence landscape of a persistence diagram", "topics": [ "hg_landscape" ] }, { "page": "hg_laplacian", "title": "Normalized hypergraph Laplacian", "topics": [ "hg_laplacian" ] }, { "page": "hg_line_graph", "title": "The s-line graph of a hypergraph", "topics": [ "hg_line_graph" ] }, { "page": "hg_markov_stability", "title": "Markov stability of the states of a transition network", "topics": [ "hg_markov_stability" ] }, { "page": "hg_membership", "title": "Soft topic membership from the spectral embedding", "topics": [ "hg_membership", "plot.hypergraphs_membership" ] }, { "page": "hg_mmsbm", "title": "Mixed-membership communities of a hypergraph (Hy-MMSBM)", "topics": [ "hg_get.net_hg_mmsbm", "hg_mmsbm", "plot.net_hg_mmsbm", "print.net_hg_mmsbm" ] }, { "page": "hg_modularity", "title": "Hypergraph modularity of a partition", "topics": [ "hg_modularity" ] }, { "page": "hg_motifs", "title": "Y, T and O motif census with a hypergraph configuration null", "topics": [ "hg_get.hypergraphs_motifs", "hg_motifs", "plot.hypergraphs_motifs" ] }, { "page": "hg_neural", "title": "Hypergraph neural network classifier (HGNN)", "topics": [ "hg_neural" ] }, { "page": "hg_null_test", "title": "Degree-preserving null-model test of 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